Integrated Data Repository System: Fusion, Learning and Sharing
Jeferson Lopes
2 a
, Giancarlo Lucca
1 b
, Rafael Huszcza
2 c
, Amanda Mendes
2 d
Eduardo N. Borges
2 e
, Pablo D. B. Guilherme
3 f
and Leandro A. Pereira
4 g
Mestrado em Engenharia Eletr
onica e Computac¸
ao, Universidade Cat
olica de Pelotas, Pelotas, Brazil
Centro de Ci
encias Computacionais (C3), Universidade Federal do Rio Grande (FURG), Rio Grande, Brazil
Centro de Educac¸
ao, Humanidades e Ci
encias Biol
ogicas, Universidade Estadual do Paran
a (UNESPAR), Paranagu
Eixo Tecnol
ogico de Meio Ambiente, Instituto Federal do Paran
a (IFPR), Paranagu
a, Brazil
Data Repository, Machine Learning, Deep Learning, Geographic Information System.
Currently, an enormous volume of data is being generated from diverse sources, including sensors and social
media. Effectively managing this unprecedented scale of data and deriving meaningful insights from these
extensive datasets present a significant challenge for computer scientists. In this context, this paper outlines
the development and documentation of a project dedicated to actively contributing to these critical data-driven
initiatives. The described system integrates the features of a scientific data repository with a suite of data
science methods, machine learning tools, and resources for geographic data visualization. By consolidating
these functionalities on a single platform, users can streamline their workflow and extract insights from data
more efficiently. This integrated approach facilitates seamless transitions from data storage to model training
and analysis, fostering collaboration and facilitating knowledge sharing among researchers and practitioners.
In this work, we highlight the system’s key features, focusing on the datasets repository and the machine
learning module as central components of our platform.
It is widely recognized that data has been generated
at increasingly high rates, especially due to advances
in mobile devices and digital sensors, which have
greatly facilitated data collection. Hence, large-scale
datasets have been curated and are widely employed
across various domains (Yaqoob et al., 2016).
Managing this unprecedented volume of data in
Big Data era poses a significant challenge for com-
puter scientists (Al Aghbari., 2015). Hence, machine
learning algorithms (Tan et al., 2005) have risen as
indispensable tools for uncovering intricate patterns
in this vast and complex datasets, providing valuable
assistance to professionals in diverse data-intensive
fields, including medicine, biology and beyond.
However, to effectively harness the wealth of data
through machine learning is a challenge for non-
computer science end-users. Hence, platforms must
transcend mere data repositories and seamlessly inte-
grate machine learning tools for extracting meaning-
ful insights. Recognizing the expense of organizing
databases, a focus on cost-effective solutions involv-
ing database management and artificial intelligence is
crucial for technological advancement.
The paramount importance of services in this do-
main is evident in the growth of platforms like Kag-
gle (Kaggle, 2023) and Hugging Face (Hugging Face,
2023). These platforms succeed in addressing chal-
lenges with a versatile approach that spans multiple
domains, assisting in the integration of information
among stakeholders.
Nevertheless, these feature-rich tools can be in-
timidating for users who are not familiar with the field
of Computer Science, including scientists from other
areas, since they tend to require in-depth technical
knowledge for operation. Moreover, another chal-
Lopes, J., Lucca, G., Huszcza, R., Mendes, A., Borges, E., Guilherme, P. and Pereira, L.
Integrated Data Repository System: Fusion, Learning and Sharing.
DOI: 10.5220/0012733700003690
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 26th International Conference on Enterprise Information Systems (ICEIS 2024) - Volume 1, pages 409-416
ISBN: 978-989-758-692-7; ISSN: 2184-4992
Proceedings Copyright © 2024 by SCITEPRESS Science and Technology Publications, Lda.
lenge faced by many is the need for local installa-
tion of these systems, which may involve complicated
configurations and specific hardware requirements.
Therefore, an efficient dataset management plat-
form that integrates data science tools, including ar-
tificial intelligence algorithms, yet remains simple
and intuitive for all users, is crucial. The developed
system, namely Data Symbion Environmental Intelli-
, aims to encompass all of these aspects. It is
a centralized repository where users, both individuals
and legal entities, can share their data on the platform.
This enables the integration and combination of infor-
mation from different sources, increasing the chances
of extracting useful and valuable insights.
Besides being a dataset repository with integrated
machine learning tools that anyone can use, regardless
of technical knowledge, our platform is web based,
which enables users to access it from anywhere, at any
time, with internet connection. The machine learning
tools are tailored for creating and utilizing predictive
models for both tabular and visual data.
Moreover, our system can be accessed directly
through the API, which facilitates seamless integra-
tion with data collection systems, streamlining au-
tomation processes. The platform also includes a tool
for geographic visualization tailored to datasets con-
taining latitude and longitude information, facilitating
visual comprehension through interactive maps.
The general objective of this work is to present the
development and the structure of the proposed sys-
tem. The following list outlines the set of specific ob-
jectives of the project:
Offer an efficient and accessible data repository;
Integrate data from multiple sources;
Allow users to train and share supervised ma-
chine learning models, including, but not limited
to, deep learning models for image classification;
Provide tools for operations on geographic data;
Allow software and hardware developers to im-
plement graphical interfaces and/or automate pro-
cesses through the consumption of REST API;
Provide a user-friendly and welcoming interface
accessible to users from diverse domains.
The study is structured as follows: Section 2 pro-
vides a review of existing tools for data repositories
and machine learning. In Section 3, we begin by com-
paring these existing tools to our proposed system.
We then outline our system’s modules and describe
its main features. Finally, Section 4 summarizes the
contributions of our system and possible limitations.
Effectively organizing and utilizing the extensive col-
lected data in a systematic manner has a core impor-
tance in various fields. Tools focused on database
management and artificial intelligence (AI) have
shown tremendous potential in recent years (Russell
and Norvig, 2009). This section discusses existing
software that serve as data repositories, and platforms
that provide machine learning tools.
Available solutions for data management such as
, Dataverse
and ArcGIS
, are commited to
the FAIR principles (Findable, Accessible, Interoper-
able, and Reusable). This is crucial to ensure relia-
bility, quality and standardization in the organization
and sharing of data (Rocha et al., 2021).
Although repositories play an important role by
providing a centralized location for storing and shar-
ing data, effective data management requires a more
comprehensive approach (Say
ao and Sales, 2022).
In the presented context, several platforms offer AI
tools, which allow the extraction of valuable in-
sights from collected data, thus supporting decision-
making (Hachicha Belghith et al., 2020).
Artificial Intelligence (AI) is a field that focuses
on algorithms that bring computers closer to human
perception, performing automated tasks. Machine
learning (ML) algorithms are AI methods capable of
learning and improving from experience (Tan et al.,
2005). By training ML models with sufficient amount
of information, we can leverage them to recognize
patterns in the training data.
Two well recognized and open-source solutions
are Waikato Environment for Knowledge Analysis
(WEKA) (Hall et al., 2009)
and RapidMiner
mann and Klinkenberg, 2016), which provide ma-
chine learning tools for data mining used to extract
valuable insights from datasets. Also, many tools
designed for machine learning excel in addressing
challenges with a more versatile approach that spans
multiple domains. Some emerging tools in this con-
text include Kaggle
(Kaggle, 2023) and Hugging
(Hugging Face, 2023). In what follows we dis-
cuss some of them:
DSpace DSpace is a web application, that al-
lows users to publish documents and data, serving
as a long-term digital archives system. DSpace
ICEIS 2024 - 26th International Conference on Enterprise Information Systems
supports access to all types of digital content in-
cluding text, images, moving images, mpegs and
data sets. Its features make this software a good
choice for academic, non-profit, and commercial
organizations building open digital repositories.
However, despite being free and open source,
DSpace requires installation.
Dataverse Another data repository platform is
the Dataverse Project, which is an open source
web application to share, preserve, cite, explore,
and analyze research data. This tool facilitates
data sharing, empowering users to replicate oth-
ers’ work more easily.
ArcGIS One important application of data
repositories are Geographic Information System
(GIS), which is a software designed to collect,
manage, analyze, and visualize geographic data.
A GIS is particularly valuable for integrating
and organizing spatial information, covering ar-
eas such as marine ecosystems, coastal land use,
and economic activities (Randazzo et al., 2021).
One important example of GIS is the ArcGIS plat-
form. It is a software that allows secure map-
ping and spatial analysis, empowering users to
unlock geospatial insights. This software is built
on scalable and resilient technology, allowing ef-
ficient data collection, management, and analysis,
thus, facilitating decision-making by easily shar-
ing maps and apps.
WEKA An open source software that provides a
collection of machine learning algorithms for data
mining tasks. It encompasses tools for data prepa-
ration, classification, regression, clustering, asso-
ciation rules mining, and visualization. Despite
its wide range of applications, WEKA not only
requires installation of the software itself but also
needs a compatible version o Java.
RapidMiner RapidMiner data science plat-
form is designed for diverse teams to collabora-
tively generate and share data-driven insights. It
caters to various skill sets, from data scientists
to business analysts, offering a unified environ-
ment. Users can build data and machine learning
pipelines with code-free to code-friendly experi-
ences, increasing trust with interactive decision
trees and model simulators.
Kaggle Kaggle empowers users to access hun-
dreds of pre-trained machine learning models
for deployment and facilitates exploration, anal-
ysis, and sharing of high-quality datasets (Kag-
gle, 2023). This platform offers a wealth of re-
sources for machine learning enthusiasts, includ-
ing 296K high-quality public datasets, 2,200 pre-
trained ML models, and a large repository of
community-published models, data, and code.
Hugging Face Hugging Face extends its ser-
vices beyond providing a platform for model and
dataset sharing; it equips users with tools for
building, training, and deploying machine learn-
ing models based on open-source (OS) code and
technologies (Hugging Face, 2023). The plat-
form offers a wide range of libraries and frame-
works, including Transformers, Tokenizers, and
Datasets, which are designed to simplify the de-
velopment process and make it more accessible.
In real-world applications, it is essential to offer plat-
forms equipped with a comprehensive set of tools fea-
turing convenient and accessible functions. Some key
features include:
Data repository: enable users to efficiently,
safely, and securely store and manage data.
Machine learning: seamlessly integrate data
repositories with AI capabilities to extract knowl-
edge from data.
Interactive maps: offering features like inter-
active maps for data visualization is essential in
geographic systems,.
Open Source: being open source is particularly
valuable as it promotes collaboration, innovation,
and accessibility in the tech industry.
Online access: online systems that require no
installation or complex settings make technology
more accessible.
Table 1 presents a comparison of the developed
system with the other tools discussed in the previous
section considering the aforementioned criteria.
All the presented tools offer valuable services to
users, whether for storing and managing data or em-
ploying artificial intelligence for data mining. Nev-
ertheless, these systems, whether tailored specifically
for the environmental field or designed for more gen-
eral applications, may be considered overly complex
and less intuitive, particularly for users lacking famil-
iarity with the computer science domain.
Our proposed system integrates the functionali-
ties of a scientific data repository with those of a ma-
chine learning tool, enabling users to store and share
datasets and to employ them for training and using
machine learning models. Data Symbion EI incorpo-
rates a GIS module, providing access to interactive
Integrated Data Repository System: Fusion, Learning and Sharing
Table 1: Comparison of the discussed tools with the proposal.
Tool Data repository Machine Learning Interative maps Open-Source Online acess
DSpace X X X
Dataverse X X X
Rapid Miner X X
Kaggle X X X X
Hugging Face X X X X
Proposed System X X X X X
To have a more complete understanding of the
project, Figure 1 provides an overview of all screens
and features of the proposed platform. It is mapped
among screens (green) and system features (yellow).
To register in the system, users are required to
provide personal information, including email, name,
personal identification number, and password. Once
registration is complete, users can access the system
and will be redirected to the Dashboard page.
This Dashboard page presents crucial information
pertaining to the system, including details such as the
number of institutions with which the user is affiliated
and the number of registered Data Sources. Also, the
system incorporates a guide button to help users easily
navigate and access the platform’s features.
3.1 Data Repository
The system introduces a comprehensive data repos-
itory service, empowering users to securely upload
their datasets and customize sharing permissions as
desired. This platform offers a structured approach
to data storage, ensuring organization and accessibil-
ity. These datasets can then be seamlessly utilized
across various tools within the system, including data
integration, geographic information visualization, and
machine learning model training, enhancing the over-
all functionality and utility of the platform.
Users can securely upload and manage diverse
datasets, including structured data such as CSV files,
as well as unstructured data like images for deep
learning training. Additionally, CRUD (Create, Read,
Update, Delete) operations can be adeptly executed
on these repositories, thereby ensuring the efficacy of
their management and maintenance.
The system offers two types of data storage struc-
tures, namely Data Source and Image Source, allow-
ing users to categorize and structure their information
systematically. The Data Source is designed for tab-
ular data, such as that found in a spreadsheet. Users
can create a Data Source instance, define its parame-
ters such as name, description, access type and insti-
tution, and upload a CSV file.
The Image Source is a structure analogous to the
Data Source, but instead of being associated with a
CSV file, it encapsulates a set of images. For the clas-
sification task, the images are organized into folders,
each associated with one of the data classes.
To enhance collaboration and data sharing, our
platform incorporates customizable sharing permis-
sions. Users can effortlessly adjust access levels, de-
scribed in Section 3.3, allowing them to share data
with others institutions.
3.2 Data Integration
In our system, it is possible to integrate two or more
Data Sources to produce a new instance, resultant
from their combination. This is the so-called Data
Integration Module. This module allows users to per-
form two types of integration: by rows or by columns.
Integrating two or more Data Sources by row in-
volves concatenating them vertically, stacking one on
top of the other. Users can choose which columns
to include in the integrated dataset and automatically
add a new column to indicate the origin of each sam-
ple. Conversely, integrating by column enables users
to merge two or more Data Sources horizontally. This
is especially valuable when the Data Sources describe
different attributes of the same samples.
After the Data Sources are selected, it is necessary
to define the type of integration and if it’s necessary
to remove/maintain duplicated samples. The meta-
data of the new Data Source instance such as name,
description and access is also required.
3.3 Data Protection
The system includes several visibility profiles, which
enable users to control the accessibility of their Data
Sources, Image Sources and trained models. The
three available privacy modes are:
1. Private: Only its responsible can access the
data/model. So, even when it is linked to an in-
stitution, the data/model will not be shared with
anyone until it is published.
ICEIS 2024 - 26th International Conference on Enterprise Information Systems
Figure 1: General view of the Project system.
2. Public: The data/model will be shared with the
users linked to the institution with which it has an
active connection. All the other users that are not
linked to this institution will not have access.
3. Open: All the users, including the ones that are
not registered on the system will have access to
the data/model.
It is important to note that if a user does not explic-
itly choose a visibility setting, it is set to the default
mode, which is private.
3.4 Geographic Information
Within our platform, users are empowered to ex-
plore and analyze geographical data through an in-
teractive maps section, enhancing their capabilities
in biological research and decision-making processes.
This module allows users to visualize specific Data
Sources of interest by selecting them. It can contain a
wide range of geographical data.
To ensure accurate representation, users are re-
quired to specify the columns that correspond to lati-
tude and longitude coordinates and select a label col-
umn that identifies the primary attribute associated
with each data point. This functionality allows for
the differentiation of individual points, facilitating ef-
fective data interpretation and analysis.
In Figure 2, the Geographic Visualization Module
is divided into two components. The left screen dis-
plays the settings for map creation, showing the selec-
tion of the data source and columns related to latitude,
longitude, and the attribute to be visualized. Users
can also specify the initial number of samples to be
loaded onto the map. The second screen showcases
the visualization of data points and their geographical
locations, which can be grouped for improved visual-
ization. Furthermore, a button is available for loading
additional data onto the map.
3.5 Machine Learning Tools
A model is a computational representation created by
a machine learning or deep learning algorithm during
the training process. This representation is learned
from data and, at the end of training, can be used to
make predictions on new input data.
Our system provides a powerful module to al-
low the persistence and sharing of models trained by
users. Thus, shortly after training, in both the Ma-
chine Learning and Deep Learning modules, it is pos-
sible to save the generated model in the system itself,
eliminating the need for the user to save it locally.
These persistence and sharing capabilities offer
users greater flexibility, as model training occurs in
the background and results—including the model,
its parameters, and training status—are automatically
saved in this module. This eliminates the need for
users to remain on the training page until completion.
In addition to facilitating model sharing among
users, this module enhances security, organization,
and convenience. It provides a list of all trained mod-
els and includes a filter system to simplify searches.
Integrated Data Repository System: Fusion, Learning and Sharing
Figure 2: The Geographic Visualization Module.
This tool offers the user the possibility to evaluate
the trained models and use them for inference on new
data. In order to use the model, the user can manually
input the new sample to obtain the model’s predic-
tion. This feature allows users to employ their trained
models in real-world scenarios.
The Machine Learning Module empowers users
to extract valuable insights and knowledge from their
data. By utilizing advanced analytical techniques, it is
possible to uncover patterns, trends, and relationships
within datasets. The module allows for experimen-
tation with different approaches, using various algo-
rithms and testing different parameter settings. This
functionality enables students and professionals from
diverse domains to gain a deeper understanding of
their data, empowering them to make informed de-
cisions and drive innovative research.
The system offers a range of machine learning ca-
pabilities, including classical algorithms for regres-
sion and classification and advanced deep learning al-
gorithms tailored for image classification.
3.6 Classical Machine Learning
Classical Machine Learning algorithms are tailored
to learn from structured data, such as data in spread-
sheet form. Therefore, these methods can be utilized
to learn from data stored in the Data Source structure
provided by our system.
We initially consider explainable methods, to bet-
ter describe the generated models. In this sense,
for the classification problem (Duda et al., 2000),
CART (Breiman et al., 1984) and Random Forrest
(Ho, 1995) algorithms were implemented. For the re-
gression problem (Hastie et al., 2009), we consider
the Support Vector Machines (SVM) (Cortes and Vap-
nik, 1995) and Linear Regression (Weisberg, 2005).
The classification task involves categorizing input
data into predefined classes or categories (Sen et al.,
2020). The Decision Tree (Breiman et al., 1984) al-
gorithm addresses this task by constructing a series of
rules organized in a tree-like structure. Each leaf node
in the tree corresponds to a class label. The algorithm
learns these rules from the training data, determining
the best splits to classify the data accurately.
Random Forest (Ho, 1995) is a widely used tech-
nique for solving classification tasks. It belongs to
the ensemble learning methods and works by creating
multiple decision trees. The final class prediction is
determined through a voting mechanism, where each
tree’s prediction contributes to the final outcome.
Regression tasks aim to predict a continuous value
based on a set of attributes (Sen et al., 2020). For ex-
ample, predicting the weight of a person based on a
set of body measurements is a regression task. One
of the algorithms available to address this challenge
is the Support Vector Machine (SVM) (Cortes and
Vapnik, 1995). SVM aims to find a hyperplane that
best fits the data points. The hyperplane is chosen
such that it passes through as many points as possi-
ble, while still staying within the margin of tolerance.
Another approach to finding the line that best rep-
resents the data is by using the linear regression algo-
rithm (Weisberg, 2005), also available in our system.
This is a straightforward technique that aims to find
the line that, on average, is closest to the data points.
To train a machine learning model, users need to
follow these steps:
1. Choose a Data Source;
2. Select the type of problem to be solved (classifi-
cation or regression);
3. Define the target variable to be predicted;
4. Set a random seed for reproducibility;
5. Select the attributes (i.e., columns) from the Data
Source to be considered;
6. Specify the proportion of the Data Source to be
employed for model training and evaluation.
3.7 Deep Learning Algorithms
Another important feature in our system is the Deep
Learning module. Deep learning, a subset of AI,
ICEIS 2024 - 26th International Conference on Enterprise Information Systems
Figure 3: Training a Deep Learning model.
focuses on developing models capable of complex
learning and pattern recognition tasks. This approach
uses deep neural network architectures, composed of
multiple layers of interconnected units, to automat-
ically learn hierarchical and abstract representations
of data (Goodfellow et al., 2016).
In addition to being widely applied in areas such
as natural language processing, recommendation sys-
tems, and speech recognition, deep learning models
also excel in computer vision. This field of study fo-
cuses on developing algorithms capable of extracting
knowledge from images and videos to perform tasks
such as image classification, object detection, and se-
mantic segmentation (Goodfellow et al., 2016).
The first functionality of this new module is im-
age classification, which consists in categorizing im-
ages into predefined groups. Two deep neural net-
work architectures designed for this task have been
made available: MobileNetV3 (Howard et al., 2019)
and ResNet50 (He et al., 2016). Users can employ
these algorithms to train deep learning models on im-
ages stored within a Image Source structure.
MobileNetV3, initially developed for mobile and
edge devices such as smartphones and IoT, strikes a
balance between speed and accuracy. This makes it
ideal for applications where computational resources
are limited (Howard et al., 2019).
In contrast, ResNet50 is a more robust network
architecture. Its name, ”ResNet, comes from the
concept of residual learning, which is central to its
design. This approach involves creating shortcuts,
known as skip connections, throughout the network.
These connections make it easier for information to
flow through the network without being lost or dis-
torted, ultimately improving the network’s ability to
learn and perform complex tasks (He et al., 2016).
In addition to choosing one of the two networks,
the user can also add layers to customize their archi-
tecture. After defining the training parameters, as il-
lustrated in Figure 3, the model is trained and made
available to the user for inference on new images.
Effectively managing the vast volume of data gen-
erated today is a critical and evolving field in com-
puter science. It is essential to provide end-users from
various fields with data repositories integrated with
machine learning tools to extract meaningful insights
from their data. However, existing tools are often too
complex and lack accessibility and intuitiveness for
users unfamiliar with computer science.
Our system provides a scientific data repository,
enabling users to securely store, share, and utilize
datasets for seamlessly training and deploying ma-
chine learning models. Two storage structures, Data
Source and Image Source, enable organization and
sharing of tabular and image data.
The Machine Learning Module offers various al-
gorithms and parameters for users to experiment and
better understand their data for decision-making. The
Data Integration Module merges datasets, and the GIS
module provides an interactive maps section that fa-
cilitates geographical data analysis.
While our proposed system offers a comprehen-
sive array of features to tackle various challenges in
data management and analysis, it is imperative to rec-
ognize its potential limitations. One notable con-
straint may lie in the scalability of the system, par-
ticularly when confronted with exceptionally large
datasets. Limited hardware capabilities and software
constraints could restrict the size and complexity of
Integrated Data Repository System: Fusion, Learning and Sharing
models that can be effectively trained, potentially im-
peding the exploration of highly dense model archi-
tectures or the utilization of extensive datasets.
Future works could focus on enhancing the scala-
bility and efficiency of ML algorithms for processing
large-scale datasets stored in scientific repositories.
Moreover, leveraging emerging technologies such as
federated learning and edge computing holds promise
for enabling distributed and real-time analysis could
be an interesting feature for the system.
The authors would like to thank Fundac¸
ao de Am-
a Pesquisa do Estado do Rio Grande do Sul -
FAPERGS, Conselho Nacional de Desenvolvimento
ıfico e Tecnol
ogico - CNPq (3305805/2021-
5, 23/2551-0000126-8), Fundac¸
ao Grupo Botic
(camp 001 2021) and Fundac¸
ao Arauc
aria de Apoio
ao Desenvolvimento Cient
ıfico e Tecnol
ogico do Es-
tado do Paran
a (FA).
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